feat(overlay): 覆盖网络线序①–⑮ 代码与测试产物入库
覆盖网络线累积产物（此前只在工作区、未入版本库）：
- 新增 relay 子系统 src/net/relay/**（wire/duplex/server/client/dialer/switcher/directory/identity/keys/placement/network/addr-override/main/index）
- 新增 src/worker/relay-tunnel.ts、src/web/routes/overlay.ts
- 新增观测/演练脚本 overlay-probe、overlay-failover-drill、overlay-keyring、overlay-holepunch、overlay-jitter、overlay-wan、overlay-relaykey-add、relay-mem-calibrate
- 新增测试 12 个（relay / relay-failover / remote-spawner / instance-port / overlay-{network,auth,bootstrap,identity} / remote-user-fs 等）
验收基线：npm test = 162 pass / 0 fail / 1 skip；--scene all = 12 PASS / 0 SKIP / 0 FAIL；overlay-probe = 12/12
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#!/usr/bin/env node
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/**
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* 覆盖网络 序⑥ · S6 `MEM_PER_HOST_MB` 校准（⛔ 一次性脚本、不进产品路径）。
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*
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* ## 为什么必须放大测
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* 现网 `used = 2` ⇒ relay 的 RSS（72888 KB）**只反映 Node 基座**，参数表 §5.1 的
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* "2 MB/台" 是**推导值不是实测**。本脚本把 N 拉到 2/10/25/50/100，量 RSS 斜率。
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*
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* ## 干净方案（⛔ 不污染生产）
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* - **relay 起在本机回环**（独立进程、独立高层口号，与生产的 20080 无关）；
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* - **N 个合成 client 跑在"本脚本进程内"** —— 这是刻意的：只要被测量的对象是 **relay 子进程**
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* 的 RSS，client 的内存长在本进程里 ⇒ **不进被测量**。（若让 client 也各自起进程，
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* 机器上会多出 100 个 Node 基座 ≈ 4 GB，纯浪费。）
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* - 全程 **127.0.0.1**，零公网面 ⇒ ⛔ 不动 47 的任何配置（S6 权限附注）。
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*
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* ## 判据
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* 线性回归 `RSS(N) = a + b·N`，`R² ≥ 0.9` 方为有效；否则**如实报告跳点**，⛔ 不许硬套斜率。
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*
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* 用法：node scripts/relay-mem-calibrate.mjs [--base 19000] [--port 23456]
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*
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* @module scripts/relay-mem-calibrate
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*/
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import { spawn } from 'node:child_process'
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import { randomBytes } from 'node:crypto'
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import { mkdtempSync, writeFileSync, rmSync } from 'node:fs'
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import { tmpdir } from 'node:os'
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import { join, dirname } from 'node:path'
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import { fileURLToPath, pathToFileURL } from 'node:url'
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const HERE = dirname(fileURLToPath(import.meta.url))
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const REPO = join(HERE, '..')
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function arg(name, dflt) {
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const i = process.argv.indexOf(`--${name}`)
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return i >= 0 ? process.argv[i + 1] : dflt
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}
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const PORT = Number(arg('port', 23456))
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const BASE = Number(arg('base', 19000))
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const SPAN = Number(arg('span', 3000))
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const POINTS = (arg('points', '2,10,25,50,100')).split(',').map(Number)
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const logs = []
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const { RelayClient } = await import(pathToFileURL(join(REPO, 'lib/net/relay/client.js')).href)
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const tmp = mkdtempSync(join(tmpdir(), 'relay-mem-'))
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const keysFile = join(tmp, 'keys.json')
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const keys = {}
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const secrets = {}
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for (let i = 1; i <= Math.max(...POINTS); i++) {
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const sec = randomBytes(32).toString('hex')
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secrets[i] = sec
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keys[`ops/w-${i}`] = sec
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}
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writeFileSync(keysFile, JSON.stringify(keys, null, 2))
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const relay = spawn(
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process.execPath,
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[
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join(REPO, 'lib/net/relay/main.js'),
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'--port',
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String(PORT),
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'--keys-file',
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keysFile,
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'--base',
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String(BASE),
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'--span',
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String(SPAN),
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'--max-hosts',
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'0',
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],
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{ cwd: REPO, stdio: ['ignore', 'pipe', 'pipe'] },
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)
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relay.stdout.on('data', (d) => logs.push(String(d)))
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relay.stderr.on('data', (d) => logs.push(String(d)))
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const sleep = (ms) => new Promise((r) => setTimeout(r, ms))
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async function status() {
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const r = await fetch(`http://127.0.0.1:${PORT}/status`)
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return r.json()
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}
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async function used() {
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try {
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return (await status()).capacity.used
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} catch {
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return -1
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}
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}
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async function rssKbMedian(pid, times = 7, gapMs = 500) {
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const arr = []
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for (let i = 0; i < times; i++) {
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arr.push(await rssKbOnce(pid))
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await sleep(gapMs)
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}
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const s = arr.filter((x) => Number.isFinite(x)).sort((a, b) => a - b)
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return { median: s[Math.floor(s.length / 2)], min: s[0], max: s[s.length - 1], n: s.length }
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}
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async function rssKbOnce(pid) {
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if (process.platform === 'win32') {
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const r = await new Promise((res) => {
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const p = spawn('powershell', ['-NoProfile', '-Command', `(Get-Process -Id ${pid}).WorkingSet64`])
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let o = ''
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p.stdout.on('data', (d) => (o += d))
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p.on('close', () => res(o.trim()))
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})
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return Math.round(Number(r) / 1024)
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}
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const r = await new Promise((res) => {
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const p = spawn('sh', ['-c', `ps -o rss= -p ${pid}`])
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let o = ''
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p.stdout.on('data', (d) => (o += d))
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p.on('close', () => res(o.trim()))
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})
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return Number(r)
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}
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const clients = []
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const samples = []
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const results = { platform: process.platform, node: process.version, points: [], raw: [] }
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for (const target of POINTS) {
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while (clients.length < target) {
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const i = clients.length + 1
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const c = new RelayClient({
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url: `ws://127.0.0.1:${PORT}/dshs-relay`,
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hostId: `w-${i}`,
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networkId: 'ops',
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secret: secrets[i],
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ports: [BASE + i],
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})
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c.start()
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clients.push(c)
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}
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let u = -1
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for (let i = 0; i < 60; i++) {
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u = await used()
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if (u >= target) break
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await sleep(500)
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}
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await sleep(1000)
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const rss = await rssKbMedian(relay.pid)
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samples.push({ n: clients.length, used: u, rssKb: rss.median, rssMin: rss.min, rssMax: rss.max, nSamples: rss.n })
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process.stderr.write(`[mem] N=${clients.length} used=${u} RSS=${rss.median} KB (min ${rss.min} / max ${rss.max})\n`)
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}
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// 线性回归
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const n = samples.length
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const sx = samples.reduce((a, s) => a + s.n, 0)
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const sy = samples.reduce((a, s) => a + s.rssKb, 0)
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const sxx = samples.reduce((a, s) => a + s.n * s.n, 0)
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const sxy = samples.reduce((a, s) => a + s.n * s.rssKb, 0)
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const b = (n * sxy - sx * sy) / (n * sxx - sx * sx)
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const a = (sy - b * sx) / n
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const my = sy / n
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const ssTot = samples.reduce((acc, s) => acc + (s.rssKb - my) ** 2, 0)
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const ssRes = samples.reduce((acc, s) => acc + (s.rssKb - (a + b * s.n)) ** 2, 0)
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results.points = samples
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results.fit = {
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interceptKb: Math.round(a),
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slopeKbPerHost: Math.round(b * 10) / 10,
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r2: Math.round((1 - ssRes / ssTot) * 10000) / 10000,
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}
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results.projected = {
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memPerHostMbCeil20: Math.ceil((b / 1024) * 1.2 * 10) / 10,
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at225HostsMb: Math.round((a + b * 225) / 1024),
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}
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const last = samples[samples.length - 1]
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process.stdout.write(`### RESULT ### ${JSON.stringify({ ...results, lastUsed: last.used })}\n`)
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for (const c of clients) c.stop()
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relay.kill()
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await sleep(500)
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rmSync(tmp, { recursive: true, force: true })
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process.exit(0)
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